The ability to act quickly and decisively in today's increasingly competitive marketplace is critical to the success of any organization. The volume of data that is available to organizations is rapidly increasing and frequently overwhelming. However, proper management and retrieval of relevant information creates a substantial advantage to the organization commonly referred to as business intelligence. The need to carve intelligent and useful information out of the large volumes of data presents various challenges. One challenge is to avoid inundating an individual with unnecessary information. Another challenge is to ensure all relevant information is available in a timely manner.
One known approach to addressing these and other challenges is known as data warehousing. Data warehouses, relational databases, and data marts are becoming important elements of many information delivery systems because they provide a central location where a reconciled version of data extracted from a wide variety of operational systems may be stored. As used herein, a data warehouse should be understood to be an informational database that stores shareable data from one or more operational databases of records, such as one or more transaction-based database systems. A data warehouse typically allows users to tap into a business's vast store of operational data to track and respond to business trends that facilitate forecasting and planning efforts. A data mart may be considered to be a type of data warehouse that focuses on a particular business segment.
Decision support systems (DSS) have been developed to efficiently retrieve selected information from data warehouses, thereby providing business intelligence information to the organization. One type of decision support system is known as an on-line analytical processing system (“OLAP”). In general, OLAP systems analyze the data from a number of different perspectives and support complex analyses against large input data sets.
In conventional OLAP systems, business intelligence queries originating from client computer systems (such as, e.g., world wide web client systems, desktop client systems, wireless client systems, etc.) interface with an OLAP application or other business intelligence server through a COM application program interface (API). More generally, conventional systems are typically built around proprietary API's and communication protocols, i.e. not only using COM-compliant interfaces. One drawback of using a proprietary protocol is that the data format is not client-independent. That is, multiple API's are needed to request and display data in various formats including the web, email, instant messaging, wireless, etc. Requiring multiple protocols adds complexity and expense to the system. Another drawback of utilizing the former proprietary protocols is the difficulty of integrating with other systems. Another drawback is the lack of a standard and the corresponding level of knowledge required to make use of one or more proprietary technologies.
Accordingly, existing OLAP systems fail to provide for a method and system for enabling the exchange of business intelligence information over a computer network through a robust and scalable methodology.